Agents often lack the context to work well in large, complex codebases. Without clear goals and standards, they guess.
How Sonar helps
Sonar Vortex gives agents codebase context and constraints before they write, so the change fits your design on the first attempt.
02 / 04 · Guiderails
Guiderails: that guide and govern
Confidence gap to close
Agents need constraints that hold as they write, not after. Without them, large codebases drift and LLM judges are left to decide what is acceptable.
How Sonar helps
Sonar Vortex delivers your architecture, coding standards, security policy and approved libraries to the agent before it writes a line.
03 / 04 · Verification
Verification: thorough, independent, fixed
Confidence gap to close
Even the best models make mistakes, and errors compound on longer tasks. LLM reviewers are inconsistent, and attackers are moving faster.
How Sonar helps
SonarQube applies the same quality profiles across every agent and model, with Gitar for agentic review and Hunter Agent and Remediation Agent in the background.
04 / 04 · Evidence
Evidence: consistent, auditable, repeatable
Confidence gap to close
People tend to trust AI output even when it is wrong. Without a consistent record of every check, there is nothing to defend to an auditor.
How Sonar helps
SonarQube quality gates, with an auditable record of every check. Time stamped, traceable, tied to the commit, defensible.
Trusted by over 7M developers and 75% of the Fortune 100
Explore our offerings
Everything you need to verify AI code
SonarQube
Code verification and governance
The most comprehensive and accurate zero trust code verification platform. Provides multi-layered review of all code, and findings that are consistent, explainable, repeatable, and auditable.
Precise, efficient software supply chain and dependency assessment (SCA) to raise your security posture, preventing agents from introducing third party vulnerabilities.
Inspects every PR, answers questions in context, and applies fixes to keep reviews moving. Automatically diagnoses CI failures and enforces review policies you write in plain language.
Gives AI coding agents the context and constraints they need when they write, verifies their output in real time, and provides immediate feedback when something needs fixing.
Autonomously clears tech debt from your existing codebase by opening verified, ready-to-merge pull requests. Each fix is scanned and verified by Sonar's engine before you see it.
An AI-powered agent that runs a deep analysis of your SonarQube projects, hunting broken access control, business logic, and authentication flaws that traditional analysis was never built to catch.
The Agent Centric Development Cycle puts the formula to work
Agents get the right context and constraints as they write. Every change receives an independent review across reliability, security, quality, and compliance. Findings get verified fixes before the change ships. Each check produces evidence.
Agentic Loop
VerifyGuideSolve
CI Verification Loop
VerifyGuideSolve
Code Maintenance Loop
VerifyGuideSolve
Fewer iterations, tokens, and build failures
Optimize code generated within the agentic sandbox. Improve agent effectiveness, reduce token costs, improve output quality, and reduce risk.
Proven in the Agentic Loop
36%
Up to 36% lower token consumption
92%
Up to 92% fewer issues in committed code
Sources: Sonar, “Cut your coding agent’s cost with Sonar Vortex,” June 30, 2026; Sonar, “Introducing Sonar Vortex and the SonarQube Remediation Agent,” June 30, 2026
Automate green PRs
Zero-trust, multi-layered verification ensures code meets quality, security & architecture requirements before it is merged. Enable high-velocity, high-volume fixes with confidence.
Proven in the CI Loop
30%
higher share of SonarQube users say AI has improved code security
8%
Up to 8% lower token use on cleaner codebases
Sources: Sonar State of Code Developer Survey 2027 (n=1,026; 62% of SonarQube users vs. 48% of non-users); Sonar, “A cleaner codebase results in less token usage,” May 14, 2026
Harden your code in the background
Agents continuously analyze merged code, identifying and remediating technical debt in the background. This keeps the codebase maintainable and improves the code agents learn from.
Proven in the Maintenance Loop
1,000 hrs
of tech debt cleared in 45 days with Remediation Agent
200+
zero-days in logic issues found by Hunter Agent
Sources: Sonar enterprise fintech customer, 2026 (customer-reported); Sonar, “SonarQube Hunter Agent is now GA,” Aug 27, 2026
What Sonar users are saying
Trusted by 7M+ developers
“
We’re not just keeping quality high; we’re actually able to go faster… AI makes it easier to deliver velocity, but only if you provide the right context from tools like SonarQube.
Stephen ByrnesDistinguished EngineerCisco
“
Overall I love the tool and I’m excited to dial up our usage, particularly as tools like Claude Code gain much wider adoption and we may be forced to reckon with the quality of what we’re creating.
Eliott WeiserSr. Engineering ManagerSirius XM
“
With over 2,000 repos, manual enforcement isn’t feasible… now, every pull request automatically goes through quality gate checks, security analysis, and secret detection.
Pravien SammandhankumarHead of DevOpsFreshworks
“
The central verification platform is how we… avoid that trade-off [between speed and safety]. It keeps the checks early. It keeps them consistent, creates visibility so the devs can move quickly.
Abhay SharmaHead of Cloud and DevOpsAustralian Unity
“
As we move toward using AI tooling for code generation, it is reassuring to know that all our code is checked and scanned to provide a sanity check on the quantity of code being produced.
Sarah BurgessLead Product Manager, SecurityXero
Gartner® names Sonar a Magic Quadrant™ Leader
AI is generating code faster than teams can govern it. Sonar was named a Leader, and placed highest on Ability to Execute. We built the verification layer the AI development cycle actually needs.
SonarQube users are more likely to say AI coding has had a very positive impact on…
Code security
81%81%
more likely
Outage frequency
76%76%
more likely
Time to market
34%34%
more likely
Source: Sonar State of Code Developer Survey 2027 (n=1,026; 319 SonarQube users, 707 non-users). Top box only: share saying AI coding had a “very positive” impact. “More likely” is the relative difference vs. non-users.
*AI outage risk based on the 2026 Sonar State of Code Developer Survey. Token usage reduction based on Sonar research on agentic coding workflows; results vary by codebase.
